






"""simple docstring"""





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

_SCREAMING_SNAKE_CASE   :							List[str]          =		logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE   :							Tuple          =		{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_SCREAMING_SNAKE_CASE   :							str          =		{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

_SCREAMING_SNAKE_CASE   :							List[Any]          =		{'''facebook/blenderbot-3B''': 128}






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Tuple		=					VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		:	int		=					PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		:	Any		=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					["""input_ids""", """attention_mask"""]
    SCREAMING_SNAKE_CASE		:	Any		=					BlenderbotTokenizer






    def __init__(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				Dict=None			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=None			,		__SCREAMING_SNAKE_CASE	:				str=None			,		__SCREAMING_SNAKE_CASE	:				Tuple="replace"			,		__SCREAMING_SNAKE_CASE	:				Tuple="<s>"			,		__SCREAMING_SNAKE_CASE	:				Optional[int]="</s>"			,		__SCREAMING_SNAKE_CASE	:				Optional[int]="</s>"			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]="<s>"			,		__SCREAMING_SNAKE_CASE	:				str="<unk>"			,		__SCREAMING_SNAKE_CASE	:				Tuple="<pad>"			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]="<mask>"			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=False			,		__SCREAMING_SNAKE_CASE	:				int=True			,		**__SCREAMING_SNAKE_CASE	:				List[str]			,		)	->						List[str]:
      super().__init__(
          __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		tokenizer_file=__SCREAMING_SNAKE_CASE			,		errors=__SCREAMING_SNAKE_CASE			,		bos_token=__SCREAMING_SNAKE_CASE			,		eos_token=__SCREAMING_SNAKE_CASE			,		sep_token=__SCREAMING_SNAKE_CASE			,		cls_token=__SCREAMING_SNAKE_CASE			,		unk_token=__SCREAMING_SNAKE_CASE			,		pad_token=__SCREAMING_SNAKE_CASE			,		mask_token=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE			,		trim_offsets=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE			,		)

      lowerCamelCase_       	=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
      if pre_tok_state.get('add_prefix_space'			,		__SCREAMING_SNAKE_CASE   ) != add_prefix_space:
        lowerCamelCase_       	=		getattr(__SCREAMING_SNAKE_CASE			,		pre_tok_state.pop('type'   )   )
        lowerCamelCase_       	=		add_prefix_space
        lowerCamelCase_       	=		pre_tok_class(**__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		add_prefix_space

      lowerCamelCase_       	=		'post_processor'
      lowerCamelCase_       	=		getattr(self.backend_tokenizer			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      if tokenizer_component_instance:
        lowerCamelCase_       	=		json.loads(tokenizer_component_instance.__getstate__()   )

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
          lowerCamelCase_       	=		tuple(state['sep']   )
        if "cls" in state:
          lowerCamelCase_       	=		tuple(state['cls']   )

        lowerCamelCase_       	=		False

        if state.get('add_prefix_space'			,		__SCREAMING_SNAKE_CASE   ) != add_prefix_space:
          lowerCamelCase_       	=		add_prefix_space
          lowerCamelCase_       	=		True

        if state.get('trim_offsets'			,		__SCREAMING_SNAKE_CASE   ) != trim_offsets:
          lowerCamelCase_       	=		trim_offsets
          lowerCamelCase_       	=		True

        if changes_to_apply:
          lowerCamelCase_       	=		getattr(__SCREAMING_SNAKE_CASE			,		state.pop('type'   )   )
          lowerCamelCase_       	=		component_class(**__SCREAMING_SNAKE_CASE   )
          setattr(self.backend_tokenizer			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def   UpperCamelCase			(			self	:				int   )	->						str:
      if self._mask_token is None:
        if self.verbose:
          logger.error('Using mask_token, but it is not set yet.'   )
        return None
      return str(self._mask_token   )






    @mask_token.setter
    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Tuple:
      lowerCamelCase_       	=		AddedToken(__SCREAMING_SNAKE_CASE			,		lstrip=__SCREAMING_SNAKE_CASE			,		rstrip=__SCREAMING_SNAKE_CASE   ) if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ) else value
      lowerCamelCase_       	=		value






    def   UpperCamelCase			(			self	:				List[str]			,		*__SCREAMING_SNAKE_CASE	:				Tuple			,		**__SCREAMING_SNAKE_CASE	:				Dict   )	->						BatchEncoding:
      lowerCamelCase_       	=		kwargs.get('is_split_into_words'			,		__SCREAMING_SNAKE_CASE   )
      assert self.add_prefix_space or not is_split_into_words, (
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str			,		*__SCREAMING_SNAKE_CASE	:				Tuple			,		**__SCREAMING_SNAKE_CASE	:				Union[str, Any]   )	->						BatchEncoding:
      lowerCamelCase_       	=		kwargs.get('is_split_into_words'			,		__SCREAMING_SNAKE_CASE   )

      assert self.add_prefix_space or not is_split_into_words, (
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = None   )	->						Tuple[str]:
      lowerCamelCase_       	=		self._tokenizer.model.save(__SCREAMING_SNAKE_CASE			,		name=__SCREAMING_SNAKE_CASE   )
      return tuple(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				List[int]			,		__SCREAMING_SNAKE_CASE	:				Optional[List[int]] = None   )	->						List[int]:
      lowerCamelCase_       	=		[self.sep_token_id]
      lowerCamelCase_       	=		[self.cls_token_id]

      if token_ids_a is None:
        return len(cls + token_ids_a + sep   ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				List[int]			,		__SCREAMING_SNAKE_CASE	:				Optional[List[int]] = None   )	->						List[Any]:
      return token_ids_a + [self.eos_token_id]






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				"Conversation"   )	->						List[int]:
      lowerCamelCase_       	=		[]
      for is_user, text in conversation.iter_texts():
        if is_user:
          # We need to space prefix as it's being done within blenderbot
          inputs.append(' ' + text   )
        else:
          # Generated responses should contain them already.
          inputs.append(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		'  '.join(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.encode(__SCREAMING_SNAKE_CASE   )
      if len(__SCREAMING_SNAKE_CASE   ) > self.model_max_length:
        lowerCamelCase_       	=		input_ids[-self.model_max_length :]
        logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''   )
      return input_ids












"""simple docstring"""





import functools
from typing import Any







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						list[str]					) ->							bool:

  # Validation
  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or len(_lowerCamelCase					) == 0:
    raise ValueError('the string should be not empty string'					)

  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or not all(
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and len(_lowerCamelCase					) > 0 for item in words					):
    raise ValueError('the words should be a list of non-empty strings'					)

  # Build trie
  lowerCamelCase_       	=		{}
  lowerCamelCase_       	=		'WORD_KEEPER'

  for word in words:
    lowerCamelCase_       	=		trie
    for c in word:
      if c not in trie_node:
        lowerCamelCase_       	=		{}

      lowerCamelCase_       	=		trie_node[c]

    lowerCamelCase_       	=		True

  lowerCamelCase_       	=		len(_lowerCamelCase					)

  # Dynamic programming method
  @functools.cache
  def is_breakable(_lowerCamelCase			:						int					) -> bool:
    if index == len_string:
      return True

    lowerCamelCase_       	=		trie
    for i in range(_lowerCamelCase						,							_lowerCamelCase					):
      lowerCamelCase_       	=		trie_node.get(string[i]						,							_lowerCamelCase					)

      if trie_node is None:
        return False

      if trie_node.get(_lowerCamelCase						,							_lowerCamelCase					) and is_breakable(i + 1					):
        return True

    return False

  return is_breakable(0					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




